Enhanced sensor cleaning validation

ABSTRACT

Devices, systems, and methods are provided for enhanced sensor cleaning validation. A device may determine a baseline performance measurement associated with a clean performance baseline of a sensor. The device may actuate a cleaning mechanism to remove at least a portion of an obstruction deposited on the sensor. The device may determine a first post-clean performance measurement associated with the sensor. The device may determine a degradation measurement between the baseline performance measurement and the first post-clean performance measurement, wherein the degradation measurement indicates an effectiveness of the cleaning mechanism.

TECHNICAL FIELD

This disclosure generally relates to systems and methods for enhancedsensor cleaning validation.

BACKGROUND

Some vehicles are equipped with a sensor system to collect data relatingto the current and developing state of the vehicle's surroundings. Theproper performance of a vehicle depends on the accuracy data collectedby the sensors in the sensor system. The sensor system may comprisevisual spectrum cameras, laser-ranging devices (e.g., LIDARs), thermalsensors, or other types of sensors. The sensor system enables a vehicleto detect objects and obstacles in the vicinity of the vehicle andtracks the distance, velocity, and direction of pedestrians, othervehicles, traffic lights, or similar objects in the environment aroundthe vehicle. However, these sensors may become hindered by obstructionsthat may interfere with the normal operation of the sensors as thevehicle is operated. The presence of obstructions that may limit thenormal operation of the sensors may require intervention to attempt torestore the sensors to a state close to an original state. Therefore,there is a need to enhance the operation of sensors to ensure thatobstructions do not undermine the sensor system performance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example environment of a vehicle, in accordance withone or more example embodiments of the present disclosure.

FIG. 2 depicts an illustrative schematic diagram for enhanced sensorcleaning validation, in accordance with one or more example embodimentsof the present disclosure.

FIG. 3 depicts an illustrative schematic diagram for enhanced sensorcleaning validation, in accordance with one or more example embodimentsof the present disclosure.

FIG. 4 depicts an illustrative schematic diagram for enhanced sensorcleaning validation, in accordance with one or more example embodimentsof the present disclosure.

FIG. 5 illustrates a flow diagram of a process for an illustrativeenhanced sensor cleaning validation system, in accordance with one ormore example embodiments of the present disclosure.

FIG. 6 is a block diagram illustrating an example of a computing deviceor computer system upon which any of one or more techniques (e.g.,methods) may be performed, in accordance with one or more exampleembodiments of the present disclosure.

Certain implementations will now be described more fully below withreference to the accompanying drawings, in which various implementationsand/or aspects are shown. However, various aspects may be implemented inmany different forms and should not be construed as limited to theimplementations set forth herein; rather, these implementations areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the disclosure to those skilled in the art.Like numbers in the figures refer to like elements throughout. Hence, ifa feature is used across several drawings, the number used to identifythe feature in the drawing where the feature first appeared will be usedin later drawings.

DETAILED DESCRIPTION

Sensors may be located at various positions on an autonomous vehicle.These sensors may include light detection and ranging (LIDAR) sensors,stereo cameras, radar sensors, thermal sensors, or other sensorsattached to an autonomous vehicle. These sensors may be originally usedin a lab environment to perform high precision analyses of theirperformance under certain conditions. Autonomous vehicles may be drivenin the real world and rely on the attached sensors to perform to acertain performance level under environmental factors. As the autonomousvehicles are driven in the real world, the sensors are exposed to theseenvironmental factors, but also there may be more factors than what wastested in the lab environment. This may be due to various conditionsthat may occur in the real world that are different from a controlledlab environment. This may create a new environment and variousconsequences based on this new environment. One of the challenges thatmay be faced by exposing the sensors to a new environment is attemptingto restore the sensors to a state close to the original state.

Sensors may be exposed to obstructions that could get deposited on thelenses of the sensors or may block the sensors. Some of the obstructionsmay include debris, mud, rain droplets, or any other objects that wouldhinder the normal operation of a sensor. In some embodiments, anautonomous vehicle may comprise a cleaning system associated withcleaning obstruction on sensors of the autonomous vehicle. One challengemay be determining if a cleaning system of an autonomous vehicle hasadequately cleaned the sensors and their lenses such that the sensorsare restored to a state that is close to an original state of thesensors.

Example embodiments described herein provide certain systems, methods,and devices for enhanced sensor cleaning validation.

In one or more embodiments, an enhanced sensor cleaning validationsystem may rely on performance measurement techniques employed tomeasure the performance of a sensor. For example, performancemeasurement techniques may comprise measuring the intensity of lightreflected by an object, measuring attenuation of signal strength,measuring performance metrics associated with the type of sensor, or anyother technique that returns performance measurements that may becompared using the various embodiments of this disclosure.

In one or more embodiments, an enhanced sensor cleaning validationsystem may assess whether an enhanced sensor cleaning validation systemthat has been applied to a sensor after obstructions have impacted thesensors' normal operation is performing at a validation threshold inorder to qualify the enhanced sensor cleaning validation system as anacceptable cleaning system. An enhanced sensor cleaning validationsystem may, in some scenarios, spray fluids on the obstruction then maycause airflow to push the droplets of the sprayed fluids off of thesensor, where the droplets now contain residue of the obstruction.

In one or more embodiments, an enhanced sensor cleaning validationsystem may validate an enhanced sensor cleaning validation system byproviding operational parameters associated with the performance of theenhanced sensor cleaning validation system after being exposed toenvironmental factors such as debris, mud, rain droplets, etc. Theoperational parameters provide vehicle software applications that relyon the sensor data being collected during operation of the sensor withexpected performance levels under these environmental factors in orderto process the sensor data in conjunction with these operationalparameters. For example, after the enhanced sensor cleaning validationsystem determines the expected performance levels, the softwareapplications are then capable of evaluating the sensor data based on theoperational parameters generated by the enhanced sensor cleaningvalidation system in order to implement compensation mechanisms whenevaluating the collected sensor data. In one or more embodiments, anenhanced sensor cleaning validation system may determine the performanceof the enhanced sensor cleaning validation system after the sensors havebeen exposed to environmental noise factors by using a baseline cleanperformance measurement and a post-cleaning performance measurement. Themeasure of the cleanliness of the sensor is not based on how clean asensor is but instead how well the sensor performs, even though thesensor may not be fully cleaned. That is, the performance measurementafter the sensor has been cleaned by an enhanced sensor cleaningvalidation system may be based on the performance of the sensorpost-cleaning as opposed to determining how many particles of theenvironmental noise (e.g., obstructions such as debris, mud, raindroplets, etc.) remain on the sensor or the sensor lens.

In one or more embodiments, an enhanced sensor cleaning validationsystem may determine a relative degradation in the performance of thesensor as a result of the environmental noise impacting the sensor.

In some instances, one purpose of the enhanced sensor cleaningvalidation system may be to improve one aspect of the sensorperformance. However, triggering the enhanced sensor cleaning validationsystem to clean a sensor may enhance one aspect and at the same time maydegrade another aspect of the sensor performance. For example, if thereis dust on a LIDAR sensor, the enhanced sensor cleaning validationsystem may improve one aspect of a LIDAR sensor performance but degradeanother aspect of the LIDAR sensor performance. When the enhanced sensorcleaning validation system is applied to remove the dust, the LIDARsensor performance may be improved by the removal of dust but waterdroplets that were used by spraying fluids to clean the LIDAR sensor maybecome an additional factor causing degradation of the LIDAR sensorperformance as a result of applying the enhanced sensor cleaningvalidation system.

In one or more embodiments, an enhanced sensor cleaning validationsystem may identify factors that may be used to measure sensorperformance and calculate the difference between a first performancemeasurement and a second performance measurement. However, thiscalculation may measure an attenuation loss but does not show theefficiency of the cleaning system and whether the enhanced sensorcleaning validation system has been able to achieve what it was supposedto achieve.

In one or more embodiments, an enhanced sensor cleaning validationsystem may determine a ratio that is based on initial sensor performanceand post-clean sensor performance. The enhanced sensor cleaningvalidation system may measure an initial sensor performance when thesensor is considered in a clean state. This state may be a baselineclean state that is used to determine the efficacy of an enhanced sensorcleaning validation system after the enhanced sensor cleaning validationsystem has been applied. An obstruction may be applied to the sensor ina controlled manner to establish a controlled starting point. In orderto make sure the application of obstruction is consistent throughout thevarious tests, a performance measurement ratio should be taken after theapplication of the obstruction to ensure every performance measurementratio after the application of the obstruction between the clean stateand the dirty state is within a certain range. For example, afterapplying the obstruction, the performance measurement ratio between theclean state and the dirty state may be between 0.7 and 0.95. It shouldbe understood that this range is only for illustration purposes, andother ranges for a performance measurement ratio between the clean stateand the dirty state may be set by the administrator of the test. In thecase that the performance measurement ratio between the clean state andthe dirty state is not within the range, the test may be reset byperforming a manual cleaning of the sensor lens, for example, using acleaning cloth or other means to clean the sensor lens.

After that, the enhanced sensor cleaning validation system may beapplied to remove the obstruction from the sensor. The enhanced sensorcleaning validation system may facilitate a post-clean sensorperformance measurement. The enhanced sensor cleaning validation systemmay facilitate a division of the post-clean sensor performancemeasurement and the initial sensor performance measurement. Theresulting ratio of performance may indicate how well the enhanced sensorcleaning validation system has performed. Multiple ratio values may beobtained at different measurement instances, which would then beevaluated to determine the validation of the enhanced sensor cleaningvalidation system. For example, a predetermined number of detectedpoints may be expected when a sensor is 100% clean (e.g., 100 points onthe target). In that case, the baseline clean performance measurementmay be considered as 100 points. An obstruction may be applied to thetarget, for example, debris, mud, rain droplets, or other types ofenvironmental noise. However, it should be understood that a clean statemay not be 100% clean as some minimal debris may persist even when asensor lens is manually cleaned, for example, with a cleaning cloth. Itis important to note that the baseline clean performance measurement maybe determined by measuring a clean lens multiple times to determine anaverage clean performance measurement, which would then be used as thebaseline to compare other measurements (e.g., after applying obstructionor after removing the obstruction) to it. An enhanced sensor cleaningvalidation system may be initiated to clean the sensor to attempt toremove the obstruction. An enhanced sensor cleaning validation systemmay determine a post-cleaning sensor performance measurement after theenhanced sensor cleaning validation system has completed its function.For example, the post-cleaning sensor performance measurement maydetermine a detection of 90 points off of the target. The enhancedsensor cleaning validation system may determine the ratio of thebaseline clean measurement relative to the post-cleaning measurement. Inthis example, the ratio may be equal to 90 divided by 100, which is 0.9.This use of a ratio allows the various measurements that may be takenover time to stay relative to the baseline. If only a differencemeasurement is taken (e.g., 100−90=10 points), this differencemeasurement may be deceiving because depending on the type of sensor,the difference may not be scalable to a measurement take for anothersensor under similar conditions. However, using a ratio, it is possibleto scale the ratio independently from the type of sensor and the type ofcondition.

In one or more embodiments, a sensor system may collect point clouds,which may be transformed into metrics that may be used to evaluate theperformance of the sensor. Point clouds are essentially datasets thatrepresent objects or space. Point clouds collate a large number ofsingle spatial measurements into a dataset that can then represent awhole. Point clouds are most commonly generated using 3D laser scannersand LiDAR technology and techniques. The enhanced sensor cleaningvalidation system may utilize a transformation module that transformsand convert data to values that can be used in evaluating theperformance of the sensor. For example, point clouds may be convertedinto intensity loss or refraction of points associated with a target.The attenuation may be caused by the obstruction on the sensor, and therefraction may be caused by droplets on the sensor.

In one or more embodiments, an enhanced sensor cleaning validationsystem may determine, when the obstruction is rain droplets, a ratiothat is based on an initial sensor performance before rain droplets arepresent on the sensor and a post-clean sensor performance after thepassage of a time period with or without airflow. In this scenario, theenhanced sensor cleaning validation system does not get triggered tospray fluids on the sensor since rain droplets may be removed with orwithout airflow. However, the enhanced sensor cleaning validation systemmay control the airflow by adjusting the velocity air velocity directedto the sensor. This also may depend on the rain rate and how quickly thesensor can recover during a rain condition as more and more droplets getdeposited on the sensor.

In one or more embodiments, an enhanced sensor cleaning validationsystem may determine a sensor performance measurement before a raincondition, which may be considered as a baseline clean performancemeasurement. A measurement may be taken at a predetermined interval, asthe rain droplets been deposited at a certain rain rate to determine arain sensor performance measurement. Over time, droplets may be removedfrom the sensor due to evaporation or due to having airflow against thesensor. Data points may be collected at the predetermined interval. Aratio of sensor performance measurement is then compiled of the variousdata points at the predetermined interval. For example, in a rainmitigation system, rain droplets are hitting the lens of a LIDAR sensorthat may reduce its performance as the vehicle operates in the rain. Forexample, the LIDAR sensor's reduced performance may go from 100% to 95%at a specific time instance. An enhanced sensor cleaning validationsystem may evaluate the desired airflow to remove rain droplets at arate fast enough compared to the rain rate. Measurements may be takenwithout airflow, and measurements will be taken with airflow. Theairflow velocity may be adjusted in order to improve the LIDAR sensor'sperformance. In some embodiments, the enhanced sensor cleaningvalidation system may determine mean values and standard deviationvalues associated with 1) no rain, 2) rain with a predetermined airflow,and 3) no airflow. The mean value/standard deviation may be determinedbased on determining the mean value of a plurality of ratio measurementsof sensor performance measurement. These mean values may be displayedusing an output device to display a plot comparing the mean values underthese conditions in order to validate the rain mitigation system.

In one or more embodiments, an enhanced sensor cleaning validationsystem may evaluate sensor and cleaning system performance, but it mayalso be used to evaluate the performance of an on-vehicle obstructiondetection system that triggers the cleaning system in a normaloperation. It should be noted that the enhanced sensor cleaningvalidation system may be able to achieve very high performancemeasurement resolution because it can compare back to controlledbaseline data and large data sets. The on-vehicle obstruction detectionsystem may need to perform without this additional information. Theperformance of the on-vehicle obstruction detection system performancemay be evaluated using the enhanced sensor cleaning validation system toprovide information on the accuracy of the system (ratio offalse-positives vs false-negatives) given the validation thresholdvalue. The above descriptions are for purposes of illustration and arenot meant to be limiting. Numerous other examples, configurations,processes, etc., may exist, some of which are described in greaterdetail below. Example embodiments will now be described with referenceto the accompanying figures.

FIG. 1 illustrates example environment 100 of a vehicle 102, inaccordance with one or more example embodiments of the presentdisclosure.

Referring to FIG. 1, there is shown a vehicle 102 comprising one or moresensors 125 (e.g., 125 a, 125 b, 125 c, 125 d, 125 e, or other sensorsnot shown in the FIG. 1). The one or more sensors 125 may be associatedwith a plurality of cameras, emitters, and sensors. The one or moresensors 125 may be connected to the vehicle 102 (e.g., at variouslocations on the vehicle 102). In this environment 100, there shown thatone or more sensors 125 include cameras such as stereo. The stereocameras may capture images of objects in the vicinity and around thevehicle 102. Other emitters and sensors may transmit and/or receive oneor more signals in order to detect and/or capture information associatedwith objects in the vicinity and around the vehicle 102. For example, aLIDAR sensor may transmit a LIDAR signal (e.g., light or anelectromagnetic wave), a radar uses radio waves in order to determinedistances between the vehicle and objects in the vicinity of thevehicle, and a thermal sensor may capture temperature (e.g., based on anemitted and detected infrared signal or other laser signals).

In one or more embodiments, the one or more sensors 125 may includeLIDAR 122. Some examples of a LIDAR such as Geiger mode LIDAR,ground-based LIDAR, large footprint LIDAR, small footprint LIDAR, or thelike. The one or more sensors 125 may include camera(s) 124 such asstereo cameras that may capture images in the vicinity of the vehicle102. The one or more sensors 125 may include a thermal sensor 126, suchas thermistors, resistance temperature detectors, thermocouples,semiconductors, or the like. Further, the one or more sensors 125 mayinclude a radar 128, which may be any radar that uses radio waves tocapture data from objects surrounding the vehicle 102. The one or moresensors 125 may also include one or more processor(s) 132. The one ormore processor(s) 132 may control the transmission and reception ofsignals associated with the LIDAR 122, the camera(s) 124, the thermalsensor 126, and the radar 128. The various sensors of the one or moresensors 125, when operating under normal conditions, should performaccording to its intended use. However, the vehicle 102 may be subjectedto environmental obstructions such as debris, mud, rain droplets, or anyother objects that would hinder the normal operation of the one or moresensors 125. Under such environmental obstructions, the environmentalobstructions may underperform due to obscuring the path of signals beingsent and/or received by the one or more sensors 125. This would resultin the data received from these various sensors to be unreliable whenprocessed by processor(s) 132.

In one or more embodiments, an enhanced sensor cleaning validationsystem may facilitate the verifying the performance of sensors (e.g.,any of the one or more sensors 125) to meet certain thresholds afterbeing exposed to the variety of obstructions that could cause theunderperformance of the one or more sensors 125. The enhanced sensorcleaning validation system may be used to validate a cleaning systemthat may be used after the obstruction has accumulated on the one ormore sensors 125 in real-life scenarios like driving vehicle 102 on aroad during such conditions. The enhanced sensor cleaning validationsystem may be used in a test environment independent of the vehicle 102by applying obstructions and the enhanced sensor cleaning validationsystem. In some examples, validating an enhanced sensor cleaningvalidation system may include determining whether the enhanced sensorcleaning validation system performed to an expected level and also tocapture performance parameters associated with the enhanced sensorcleaning validation system such that they may be used during real-worldoperation and expected performance level while cleaning sensorsassociated with the vehicle. For example, vehicle 102 may be driven on aroad under environmental conditions that may cause obstructions such asdebris, mud, or rain droplets to land on the surface of the sensor. Theobstructions may impact the normal operation of the sensor. An enhancedsensor cleaning validation system may validate an enhanced sensorcleaning validation system by providing operational parametersassociated with the performance of the enhanced sensor cleaningvalidation system after being exposed to environmental factors such asdebris, mud, rain droplets, etc. The operational parameters providevehicle software applications that rely on the sensor data beingcollected during operation of the sensor with expected performancelevels under these environmental factors in order to process the sensordata in conjunction with these operational parameters. For example,after the enhanced sensor cleaning validation system determines theexpected performance levels, the software applications are then capableof evaluating the sensor data based on the operational parametersgenerated sensor cleaning validation system in order to implementcompensation mechanisms when evaluating the collected sensor data.

It is understood that the above descriptions are for purposes ofillustration and are not meant to be limiting.

FIG. 2 depicts an illustrative schematic diagram for a sensor validationenvironment 200, in accordance with one or more example embodiments ofthe present disclosure.

Referring to FIG. 2, there is shown a sensor 203 that is under test forvalidation before the sensor is utilized on a vehicle 202. In FIG. 2,there is also shown an obstruction applicator 208, an enhanced sensorcleaning validation system 210, a sensor data collection 204, aperformance module 206, a validation module 212, and an output 214.

The sensor 203 may be any of the one or more sensors 125 of FIG. 1. Thesensor 203 may be tested and validated before being placed in real-worldoperations, where the vehicle 202 is operating in a real-worldenvironment using the sensor 203 to capture data associated with objectsin the vicinity of the vehicle 202.

The sensor data collection 204 may be a data collection mechanism thatcollects data captured by the sensor 203. The sensor data may includesignal information captured after being emitted and then received by thesensor 203, in the case of the sensor 203 is a LIDAR. In the case thesensor 203 is a camera, the sensor data may be image data capture by thecamera. The sensor data may be the heat signature of an object or radardata that may be data associated with a radio signal that is transmittedafter being aimed by an antenna in a particular direction, then areceiver detects the echoes off any objects in the path of the radiosignal. The sensor data collection 204 may collect sensor data when thesensor 203 is in a clean state is free from obstructions, or it maycollect sensor data after the sensor 203 has been subjected to anobstruction.

The obstruction applicator 208 may be a means to apply obstruction tothe sensor 203. For example, the obstruction applicator 208 may apply asdebris, mud, rain droplets, or other objects that may hinder the normaloperation of the sensor 203. The obstruction applicator 208 may applyone or more obstructions at a quantity or rate set by a systemadministrator. This provides control over the amount of obstruction tobe applied in order to validate the enhanced sensor cleaning validationsystem 210 at these various quantities or rates.

The enhanced sensor cleaning validation system 210 may facilitate sprayfluids on the obstruction in order to dilute the obstruction beforeremoving it off of the sensor 203. The enhanced sensor cleaningvalidation system 210 may then cause airflow to push the droplets of thesprayed fluids off of the sensor 203, where the droplets contain residueof the obstruction after spraying fluids on the obstruction. Forexample, if the sensor 203 was subjected to mud that got deposited onthe sensor, the enhanced sensor cleaning validation system 210 may sprayfluids to break up the mud and dilute it. The enhanced sensor cleaningvalidation system 210 may then cause an airflow that pushes away thedroplets of the fluid that now contains the mud particles in order topush them off of the sensor 203.

The performance module 206 may perform performance measurements thatutilize the sensor data that was collected by the sensor data collection204 before and after the application of the obstruction using theobstruction applicator 208. The performance module 206 may applyperformance measurement techniques, which comprise measuring anintensity of light reflected by an object, measuring attenuation ofsignal strength, measuring performance metrics associated with the typeof sensor, or any other technique that returns performance measurementsthat may be compared.

The performance module 206 may determine an initial performancemeasurement of sensor 203, where the initial performance measurement isdetermined when the sensor 203 is in a clean state. The performancemodule 206 may determine a post-clean performance measurement of sensor203, where the post-clean performance measurement is determined afterthe sensor 203 has been cleaned using the enhanced sensor cleaningvalidation system 210. The initial performance measurement may be abaseline clean state that is used to determine the efficacy of anenhanced sensor cleaning validation system after the enhanced sensorcleaning validation system 210 has been applied to remove an obstructionthat may have been introduced to the sensor 203. After that, theenhanced sensor cleaning validation system 210 may be applied to removethe obstruction from the sensor 203. The performance module 206 maydetermine a ratio equal to the post-clean performance measurementdivided by the initial performance measurement of sensor 203.

The validation module 212 may provide a mechanism to qualify an enhancedsensor cleaning validation system by determining how well the cleaningsystem is performing and whether the cleaning system is at an expectedlevel of performance over multiple sample measurements. The validationmodule 212 may validate an enhanced sensor cleaning validation system byproviding operational parameters associated with the performance of theenhanced sensor cleaning validation system after being exposed toenvironmental factors such as debris, mud, rain droplets, etc. Theoperational parameters provide vehicle software applications that relyon the sensor data being collected during operation of the sensor withexpected performance levels under these environmental factors in orderto process the sensor data in conjunction with these operationalparameters. For example, after the enhanced sensor cleaning validationsystem determines the expected performance levels, the softwareapplications are then capable of evaluating the sensor data based on theoperational parameters generated sensor cleaning validation system inorder to implement compensation mechanisms when evaluating the collectedsensor data.

The output module 214 may output the performance measurement in agraphical representation using the determined ratio between thepost-clean performance measurement and the initial performancemeasurement of sensor 203.

It is understood that the above descriptions are for purposes ofillustration and are not meant to be limiting.

FIG. 3 depicts an illustrative schematic diagram for enhanced sensorcleaning validation, in accordance with one or more example embodimentsof the present disclosure.

Referring to FIG. 3, there is shown a performance ratio chart betweenthree types of sensor conditions. These three types of sensor conditionsinclude a clean state, a dirty state, and a post-clean state. The ratiois based on initial sensor performance, a dirty sensor performance, andpost-clean sensor performance. It should be noted that the ‘clean’,‘dirty’, and ‘post-clean’ states can be graphed together using the rawperformance metric, but once the performance ratio is calculated, the‘clean’ state necessarily drops off because it would always result in adistribution around 1 (because the ratio divides all of the other statesby the clean state values).

The enhanced sensor cleaning validation system may measure an initialsensor performance when the sensor is considered in a clean state, whichis a state where the sensor lens has been manually cleaned. This statemay be used as a baseline state to determine how efficient an enhancedsensor cleaning validation system is after the enhanced sensor cleaningvalidation system has been applied to remove an obstruction. Anobstruction may be applied to the sensor in a controlled manner toestablish a controlled starting point. In order to make the applicationof obstruction is consistent throughout the various tests, a performancemeasurement ratio should be taken after the application of theobstruction to ensure every performance measurement ratio after theapplication of the obstruction between the clean state and the dirtystate is within a certain range. For example, after applying theobstruction, the performance measurement ratio between the clean stateand the dirty state may be between 0.7 and 0.95. It should be understoodthat this range is only for illustration purposes, and other ranges fora performance measurement ratio between the clean state and the dirtystate may be set by the administrator of the test. In the case that theperformance measurement ratio between the clean state and the dirtystate is not within the range, the test may be reset by performing amanual cleaning of the sensor lens, for example, using a cleaning clothor other means to clean the sensor lens. Single outlier values may occurand be acceptable, but if the mean/median of the distribution or thesize of the distribution is statistically different than the othertests, they will contain additional noise in the measurement. Becausethis data is post-processed and relies on distributions, it can only bedone after-the-fact. Therefore, the data would need to be recollected(with better control over the factor that led to the variation) or ifthe result is much larger than the noise, the value could be used.

It should be understood that performance measurement techniques maycomprise measuring the intensity of light reflected by an object,measuring the attenuation of signal strength, measuring performancemetrics associated with the type of sensor, or any other technique thatreturns performance measurements that may be compared to each other todetermine the effectiveness of a system, such as an enhanced sensorcleaning validation system.

After ensuring the performance measurement ratio between the clean stateand the dirty state is within the range, the enhanced sensor cleaningvalidation system may then be applied to remove the obstruction from thesensor. The enhanced sensor cleaning validation system may facilitate apost-clean sensor performance measurement. The enhanced sensor cleaningvalidation system may facilitate a division of the post-clean sensorperformance measurement and the initial sensor performance measurement.The resulting ratio of performance may indicate how well the enhancedsensor cleaning validation system has performed. Multiple ratio valuesmay be obtained at different measurement instances, which would then beevaluated to determine the validation of the enhanced sensor cleaningvalidation system.

Looking at FIG. 3, there are shown several measurements that capture theperformance ratio at various stages of a sensor lens. That is aperformance measurement ratio between a hypothetical clean state (e.g.,100% clean) and an initial clean state (after being manually cleaned),between a dirty state and the initial clean state, a performancemeasurement ratio between a post-clean state and the initial cleanstate. For example, the group of measurements 302 may be associated withclean performance measurements, dirty performance measurements, andpost-clean performance measurements. As can be seen in the group ofmeasurements 302, a higher value means a higher performance ratio, whichindicates a better performance. In this group of measurements 302, theinitial clean performance measurement ratio is higher than the dirtyperformance measurement ratio. Also, it is shown that the post-cleanperformance measurement ratio is higher than the dirty performancemeasurement ratio. This indicates that the enhanced sensor cleaningvalidation system is performing as expected because the performancemeasurement ratio after the application of the enhanced sensor cleaningvalidation system resulted in a higher ratio than the dirty performancemeasurement ratio. Further, the post-clean performance measurement ratioshows that it stands between 0.86 and 0.93. This may be compared to avalidation threshold to determine whether the enhanced sensor cleaningvalidation system is performing up to par. For example, an enhancedsensor cleaning validation system may be considered to be performing asexpected the post-clean performance measurement ratio is anywherebetween 0.85 and 1. Looking at the group of measurements 304, there isshown that the clean performance measurement ratio is less than thedirty performance measurement ratio and the post-clean performancemeasurement ratio. This may not be as expected because one would expectthat the clean performance measurement ratio may be greater than thedirty performance measurement ratio. This could be due to variousfactors such as the type of obstruction applied, the noise introduced bythe obstruction, or other test environment factors. It should be notedthat an enhanced sensor cleaning validation system may evaluate a sensorcleaning performance, but it may also be used to evaluate theperformance of an on-vehicle obstruction detection system that triggersthe cleaning system in a normal operation. It should also be noted thatthe enhanced sensor cleaning validation system may be able to achievevery high performance measurement resolution because it can compare backto controlled baseline data and large data sets. The on-vehicleobstruction detection system may need to perform without this additionalinformation. The performance of the on-vehicle obstruction detectionsystem performance may be evaluated using the enhanced sensor cleaningvalidation system to provide information on the accuracy of the system(ratio of false-positives vs false-negatives) given the validationthreshold value.

It is understood that the above descriptions are for purposes ofillustration and are not meant to be limiting.

FIG. 4 depicts an illustrative schematic diagram for enhanced sensorcleaning validation, in accordance with one or more example embodimentsof the present disclosure.

Referring to FIG. 4, there is shown a performance ratio chart betweenvarious types of sensor conditions. These various types of sensorconditions may include a no rain state, a rain with airflow state, andrain without airflow state. Looking at the group of measurements 402, itis shown that when the sensor does not have any rain on its lens, itsperformance ratio is anywhere between 0.84 and 0.9, while theperformance ratio when rain is introduced as an obstruction to thesensor lens while having an airflow is anywhere between 0.82 and 0.86.The introduction of airflow causes the rain droplets to the removed offof the sensor lens. Further, it is shown that the performance ratio whenrain is introduced as an obstruction to the sensor lens without airflowis anywhere between 0.81 and 0.87. Additional measurements may be takenin order to capture a larger data set. Subsequently, all themeasurements may be evaluated to determine how the rain affects thesensor. That information may be useful for the vehicle's softwareapplications that rely on sensor data being collected during operationof the sensor with expected performance levels under these environmentalfactors (e.g., rain) in order to process the sensor data. For example,after the enhanced sensor cleaning validation system determines theexpected performance levels, the software applications are then capableof evaluating any captured sensor data based on the expected range inorder to implement compensation mechanisms when evaluating the capturedsensor data. It is understood that the above descriptions are forpurposes of illustration and are not meant to be limiting.

FIG. 5 illustrates a flow diagram of process 500 for an illustrativeenhanced sensor cleaning validation system, in accordance with one ormore example embodiments of the present disclosure.

At block 502, a device may determine a baseline performance measurementassociated with a clean performance baseline of a sensor. The cleanperformance baseline of the sensor may be a state of the sensor withoutthe obstruction.

At block 504, the device may actuate a cleaning mechanism to remove atleast a portion of an obstruction deposited on the sensor. Theobstruction may be at least one of mud, rain, bugs, or debris. Actuatingthe cleaning mechanism causes an application of fluid on the sensor, andcauses an airflow configured to remove droplets of the fluid containingat least part of the obstruction from the sensor.

At block 506, the device may determine a first post-clean performancemeasurement associated with the sensor.

At block 508, the device may determine a degradation measurement betweenthe baseline performance measurement and the first post-cleanperformance measurement, wherein the degradation measurement indicatesan effectiveness of the cleaning mechanism. The degradation measurementmay be a ratio between the baseline performance measurement and thefirst post-clean performance measurement. The ratio may be determined bydividing the first post-clean performance measurement by the baselineperformance measurement. Also, actuating the cleaning mechanism causesan application of an airflow that removes rain droplets at an airflowrate that exceeds a rain rate. The degradation measurement may be afirst degradation measurement. The device may determine a secondpost-clean performance measurement after a second obstruction may beapplied to the sensor. The device may determine a second degradationmeasurement based on the baseline performance measurement and the secondpost-clean performance measurement. The device may determine a meanvalue or a standard deviation based on the first degradation measurementand the second degradation measurement. The device may determine a meanvalue or a standard deviation of one or more ratio values determinedbetween one or more baseline performance measurements and one or morepost-clean performance measurements may be below a validation threshold.The device may determine that the cleaning mechanism may be in a failedstate. The device may determine a mean value or a standard deviation ofone or more ratio values determined between one or more baselineperformance measurements and one or more post-clean performancemeasurements may be above a validation threshold. The device maydetermine that the cleaning mechanism may be in a pass state.

It is understood that the above descriptions are for purposes ofillustration and are not meant to be limiting.

FIG. 6 is a block diagram illustrating an example of a computing deviceor computer system 600 upon which any of one or more techniques (e.g.,methods) may be performed, in accordance with one or more exampleembodiments of the present disclosure.

For example, the computing system 600 of FIG. 6 may represent the one ormore processors 132 and/or the one or more measurement devices of FIG.2, and therefore may assess and validate the sensors 125 of FIG. 1. Thecomputer system (system) includes one or more processors 602-606.Processors 602-606 may include one or more internal levels of cache (notshown) and a bus controller (e.g., bus controller 622) or bus interface(e.g., I/O interface 620) unit to direct interaction with the processorbus 612. An enhanced sensor cleaning validation device 609 may also bein communication with the Processors 602-606 and may be connected to theprocessor bus 612.

Processor bus 612, also known as the host bus or the front side bus, maybe used to couple the processors 602-606 and/or the enhanced sensorcleaning validation device 609 with the system interface 624. Systeminterface 624 may be connected to the processor bus 612 to interfaceother components of the system 600 with the processor bus 612. Forexample, system interface 624 may include a memory controller 618 forinterfacing a main memory 616 with the processor bus 612. The mainmemory 616 typically includes one or more memory cards and a controlcircuit (not shown). System interface 624 may also include aninput/output (I/O) interface 620 to interface one or more I/O bridges625 or I/O devices 630 with the processor bus 612. One or more I/Ocontrollers and/or I/O devices may be connected with the I/O bus 626,such as I/O controller 628 and I/O device 630, as illustrated.

I/O device 630 may also include an input device (not shown), such as analphanumeric input device, including alphanumeric and other keys forcommunicating information and/or command selections to the processors602-606 and/or the enhanced sensor cleaning validation device 609.Another type of user input device includes cursor control, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to the processors 602-606 and/or theenhanced sensor cleaning validation device 609 and for controllingcursor movement on the display device.

System 600 may include a dynamic storage device, referred to as mainmemory 616, or a random access memory (RAM) or other computer-readabledevices coupled to the processor bus 612 for storing information andinstructions to be executed by the processors 602-606 and/or the myenhanced sensor cleaning validation device 609. Main memory 616 also maybe used for storing temporary variables or other intermediateinformation during execution of instructions by the processors 602-606and/or the enhanced sensor cleaning validation device 609. System 600may include read-only memory (ROM) and/or other static storage devicecoupled to the processor bus 612 for storing static information andinstructions for the processors 602-606 and/or the enhanced sensorcleaning validation device 609. The system outlined in FIG. 6 is but onepossible example of a computer system that may employ or be configuredin accordance with aspects of the present disclosure.

According to one embodiment, the above techniques may be performed bycomputer system 600 in response to processor 604 executing one or moresequences of one or more instructions contained in main memory 616.These instructions may be read into main memory 616 from anothermachine-readable medium, such as a storage device. Execution of thesequences of instructions contained in main memory 616 may causeprocessors 602-606 and/or the enhanced sensor cleaning validation device609 to perform the process steps described herein. In alternativeembodiments, circuitry may be used in place of or in combination withthe software instructions. Thus, embodiments of the present disclosuremay include both hardware and software components.

Various embodiments may be implemented fully or partially in softwareand/or firmware. This software and/or firmware may take the form ofinstructions contained in or on a non-transitory computer-readablestorage medium. Those instructions may then be read and executed by oneor more processors to enable the performance of the operations describedherein. The instructions may be in any suitable form, such as, but notlimited to, source code, compiled code, interpreted code, executablecode, static code, dynamic code, and the like. Such a computer-readablemedium may include any tangible non-transitory medium for storinginformation in a form readable by one or more computers, such as but notlimited to read-only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; a flash memory, etc.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Such media maytake the form of, but is not limited to, non-volatile media and volatilemedia and may include removable data storage media, non-removable datastorage media, and/or external storage devices made available via awired or wireless network architecture with such computer programproducts, including one or more database management products, web serverproducts, application server products, and/or other additional softwarecomponents. Examples of removable data storage media include CompactDisc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory(DVD-ROM), magneto-optical disks, flash drives, and the like. Examplesof non-removable data storage media include internal magnetic harddisks, SSDs, and the like. The one or more memory devices 606 (notshown) may include volatile memory (e.g., dynamic random access memory(DRAM), static random access memory (SRAM), etc.), and/or non-volatilememory (e.g., read-only memory (ROM), flash memory, etc.).

Computer program products containing mechanisms to effectuate thesystems and methods in accordance with the presently describedtechnology may reside in main memory 616, which may be referred to asmachine-readable media. It will be appreciated that machine-readablemedia may include any tangible non-transitory medium that is capable ofstoring or encoding instructions to perform any one or more of theoperations of the present disclosure for execution by a machine or thatis capable of storing or encoding data structures and/or modulesutilized by or associated with such instructions. Machine-readable mediamay include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more executable instructions or data structures.

In one or more embodiments, a device comprising processing circuitrycoupled to storage, the processing circuitry may be configured to:determine a baseline performance measurement associated with a cleanperformance baseline of a sensor; actuate a cleaning mechanism to removeat least a portion of an obstruction deposited on the sensor; determinea first post-clean performance measurement associated with the sensor;and determine a degradation measurement between the baseline performancemeasurement and the first post-clean performance measurement, whereinthe degradation measurement indicates an effectiveness of the cleaningmechanism. The clean performance baseline of the sensor may be a stateof the sensor without the obstruction. The obstruction may be at leastone of mud, rain, bugs, or debris. The degradation measurement may be aratio between the baseline performance measurement and the firstpost-clean performance measurement, and wherein the ratio may bedetermined by dividing the first post-clean performance measurement bythe baseline performance measurement. Actuating the cleaning mechanismcauses an application of fluid on the sensor, and causes an airflowconfigured to remove droplets of the fluid containing at least part ofthe obstruction from the sensor. The obstruction may be rain droplets,and wherein actuating the cleaning mechanism causes an application of anairflow that removes rain droplets at an airflow rate that exceeds arain rate. The degradation measurement may be a first degradationmeasurement, and wherein the processing circuitry may be furtherconfigured to: determine a second post-clean performance measurementafter a second obstruction may be applied to the sensor; determine asecond degradation measurement based on the baseline performancemeasurement and the second post-clean performance measurement; anddetermine a mean value or a standard deviation based on the firstdegradation measurement and the second degradation measurement. Theprocessing circuitry may be further configured to: determine a meanvalue or a standard deviation of one or more ratio values determinedbetween one or more baseline performance measurements and one or morepost-clean performance measurements may be below a validation threshold;and determine that the cleaning mechanism may be in a failed state. Theprocessing circuitry may be further configured to: determine a meanvalue or a standard deviation of one or more ratio values determinedbetween one or more baseline performance measurements and one or morepost-clean performance measurements may be above a validation threshold;and determine that the cleaning mechanism may be in a pass state.

In one or more embodiments, a method may comprise: determining, by oneor more processors, a baseline performance measurement associated with aclean performance baseline of a sensor; actuating a cleaning mechanismto remove at least a portion of an obstruction deposited on the sensor;determining a first post-clean performance measurement associated withthe sensor; and determining a degradation measurement between thebaseline performance measurement and the first post-clean performancemeasurement, wherein the degradation measurement indicates aneffectiveness of the cleaning mechanism. The clean performance baselineof the sensor may be a state of the sensor without the obstruction. Theobstruction may be at least one of mud, rain, bugs, or debris. Thedegradation measurement may be a ratio between the baseline performancemeasurement and the first post-clean performance measurement, andwherein the ratio may be determined by dividing the first post-cleanperformance measurement by the baseline performance measurement.Actuating the cleaning mechanism causes an application of fluid on thesensor, and causes an airflow configured to remove droplets of the fluidcontaining at least part of the obstruction from the sensor. Theobstruction may be rain droplets, and wherein actuating the cleaningmechanism causes an application of an airflow that removes rain dropletsat an airflow rate that exceeds a rain rate. The degradation measurementmay be a first degradation measurement, and further comprising:determining a second post-clean performance measurement after a secondobstruction may be applied to the sensor; determining a seconddegradation measurement based on the baseline performance measurementand the second post-clean performance measurement; and determining amean value or a standard deviation based on the first degradationmeasurement and the second degradation measurement. The method mayfurther comprise: determining a mean value or a standard deviation ofone or more ratio values determined between one or more baselineperformance measurements and one or more post-clean performancemeasurements may be below a validation threshold; and determining thatthe cleaning mechanism may be in a failed state. The method may furthercomprise: determining a mean value or a standard deviation of one ormore ratio values determined between one or more baseline performancemeasurements and one or more post-clean performance measurements may beabove a validation threshold; and determining that the cleaningmechanism may be in a pass state.

In one or more embodiments, a non-transitory computer-readable mediumstoring computer-executable instructions which when executed by one ormore processors result in performing operations may comprise:determining a baseline performance measurement associated with a cleanperformance baseline of a sensor; actuating a cleaning mechanism toremove at least a portion of an obstruction deposited on the sensor;determining a first post-clean performance measurement associated withthe sensor; and determining a degradation measurement between thebaseline performance measurement and the first post-clean performancemeasurement, wherein the degradation measurement indicates aneffectiveness of the cleaning mechanism. The clean performance baselineof the sensor may be a state of the sensor without the obstruction. Theobstruction may be at least one of mud, rain, bugs, or debris. Thedegradation measurement may be a ratio between the baseline performancemeasurement and the first post-clean performance measurement, andwherein the ratio may be determined by dividing the first post-cleanperformance measurement by the baseline performance measurement.Actuating the cleaning mechanism causes an application of fluid on thesensor, and causes an airflow configured to remove droplets of the fluidcontaining at least part of the obstruction from the sensor. Theobstruction may be rain droplets, and wherein actuating the cleaningmechanism causes an application of an airflow that removes rain dropletsat an airflow rate that exceeds a rain rate. The degradation measurementmay be a first degradation measurement, and wherein the operationsfurther comprise: determining a second post-clean performancemeasurement after a second obstruction may be applied to the sensor;determining a second degradation measurement based on the baselineperformance measurement and the second post-clean performancemeasurement; and determining a mean value or a standard deviation basedon the first degradation measurement and the second degradationmeasurement. The operations may further comprise: determining a meanvalue or a standard deviation of one or more ratio values determinedbetween one or more baseline performance measurements and one or morepost-clean performance measurements may be below a validation threshold;and determining that the cleaning mechanism may be in a failed state.The operations further comprise: determining a mean value or a standarddeviation of one or more ratio values determined between one or morebaseline performance measurements and one or more post-clean performancemeasurements may be above a validation threshold; and determining thatthe cleaning mechanism may be in a pass state.

In one or more embodiments, an apparatus may comprise means for:determining a baseline performance measurement associated with a cleanperformance baseline of a sensor; actuate a cleaning mechanism to removeat least a portion of an obstruction deposited on the sensor;determining a first post-clean performance measurement associated withthe sensor; and determining a degradation measurement between thebaseline performance measurement and the first post-clean performancemeasurement, wherein the degradation measurement indicates aneffectiveness of the cleaning mechanism. The clean performance baselineof the sensor may be a state of the sensor without the obstruction. Theobstruction may be at least one of mud, rain, bugs, or debris. Thedegradation measurement may be a ratio between the baseline performancemeasurement and the first post-clean performance measurement, andwherein the ratio may be determined by dividing the first post-cleanperformance measurement by the baseline performance measurement. Thecleaning mechanism causes an application of fluid on the sensor, andcauses an airflow configured to remove droplets of the fluid containingat least part of the obstruction from the sensor. The obstruction may berain droplets, and wherein actuating the cleaning mechanism causes anapplication of an airflow that removes rain droplets at an airflow ratethat exceeds a rain rate. The degradation measurement may be a firstdegradation measurement, and further comprising: determining a secondpost-clean performance measurement after a second obstruction may beapplied to the sensor; determining a second degradation measurementbased on the baseline performance measurement and the second post-cleanperformance measurement; and determining a mean value or a standarddeviation based on the first degradation measurement and the seconddegradation measurement. The apparatus may further comprise: determininga mean value or a standard deviation of one or more ratio valuesdetermined between one or more baseline performance measurements and oneor more post-clean performance measurements may be below a validationthreshold; and determining that the cleaning mechanism may be in afailed state. The apparatus may further comprise: determining a meanvalue or a standard deviation of one or more ratio values determinedbetween one or more baseline performance measurements and one or morepost-clean performance measurements may be above a validation threshold;and determining that the cleaning mechanism may be in a pass state.

Embodiments of the present disclosure include various steps, which aredescribed in this specification. The steps may be performed by hardwarecomponents or may be embodied in machine-executable instructions, whichmay be used to cause a general-purpose or special-purpose processorprogrammed with the instructions to perform the steps. Alternatively,the steps may be performed by a combination of hardware, software,and/or firmware.

Various modifications and additions can be made to the exemplaryembodiments discussed without departing from the scope of the presentinvention. For example, while the embodiments described above refer toparticular features, the scope of this invention also includesembodiments having different combinations of features and embodimentsthat do not include all of the described features. Accordingly, thescope of the present invention is intended to embrace all suchalternatives, modifications, and variations together with allequivalents thereof.

The operations and processes described and shown above may be carriedout or performed in any suitable order as desired in variousimplementations. Additionally, in certain implementations, at least aportion of the operations may be carried out in parallel. Furthermore,in certain implementations, less than or more than the operationsdescribed may be performed.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

As used herein, unless otherwise specified, the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicates that different instances of like objects arebeing referred to and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or any other manner.

It is understood that the above descriptions are for purposes ofillustration and are not meant to be limiting.

Although specific embodiments of the disclosure have been described, oneof ordinary skill in the art will recognize that numerous othermodifications and alternative embodiments are within the scope of thedisclosure. For example, any of the functionality and/or processingcapabilities described with respect to a particular device or componentmay be performed by any other device or component. Further, whilevarious illustrative implementations and architectures have beendescribed in accordance with embodiments of the disclosure, one ofordinary skill in the art will appreciate that numerous othermodifications to the illustrative implementations and architecturesdescribed herein are also within the scope of this disclosure.

Although embodiments have been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the disclosure is not necessarily limited to the specific featuresor acts described. Rather, the specific features and acts are disclosedas illustrative forms of implementing the embodiments. Conditionallanguage, such as, among others, “can,” “could,” “might,” or “may,”unless specifically stated otherwise, or otherwise understood within thecontext as used, is generally intended to convey that certainembodiments could include, while other embodiments do not include,certain features, elements, and/or steps. Thus, such conditionallanguage is not generally intended to imply that features, elements,and/or steps are in any way required for one or more embodiments or thatone or more embodiments necessarily include logic for deciding, with orwithout user input or prompting, whether these features, elements,and/or steps are included or are to be performed in any particularembodiment.

What is claimed is:
 1. A device, the device comprising processingcircuitry coupled to storage, the processing circuitry configured to:determine a baseline performance measurement associated with a cleanperformance baseline of a sensor; actuate a cleaning mechanism to removeat least a portion of an obstruction deposited on the sensor; determinea first post-clean performance measurement associated with the sensor;and determine a degradation measurement between the baseline performancemeasurement and the first post-clean performance measurement, whereinthe degradation measurement indicates an effectiveness of the cleaningmechanism, wherein the degradation measurement is a ratio between thebaseline performance measurement and the first post-clean performancemeasurement, and wherein the ratio is determined by dividing the firstpost-clean performance measurement by the baseline performancemeasurement.
 2. The device of claim 1, wherein the clean performancebaseline of the sensor is a state of the sensor without the obstruction.3. The device of claim 1, wherein the obstruction is at least one ofmud, rain, bugs, or debris.
 4. The device of claim 1, wherein actuatingthe cleaning mechanism causes an application of fluid on the sensor, andcauses an airflow configured to remove droplets of the fluid containingat least part of the obstruction from the sensor.
 5. The device of claim1, wherein the obstruction is rain droplets, and wherein actuating thecleaning mechanism causes an application of an airflow that removes raindroplets at an airflow rate that exceeds a rain rate.
 6. The device ofclaim 1, wherein the degradation measurement is a first degradationmeasurement, and wherein the processing circuitry is further configuredto: determine a second post-clean performance measurement after a secondobstruction is deposited on the sensor; determine a second degradationmeasurement based on the baseline performance measurement and the secondpost-clean performance measurement; and determine a mean value or astandard deviation based on the first degradation measurement and thesecond degradation measurement.
 7. The device of claim 1, wherein theprocessing circuitry is further configured to: determine a mean value ora standard deviation of one or more ratio values determined between oneor more baseline performance measurements and one or more post-cleanperformance measurements is below a validation threshold; and determinethat the cleaning mechanism is in a failed state.
 8. The device of claim1, wherein the processing circuitry is further configured to: determinea mean value or a standard deviation of one or more ratio valuesdetermined between one or more baseline performance measurements and oneor more post-clean performance measurements is above a validationthreshold; and determine that the cleaning mechanism is in a pass state.9. The device of claim 1, wherein the processing circuitry is furtherconfigured to: implement, based on the degradation measurement, acompensation mechanism when evaluating collected sensor data.
 10. Thedevice of claim 1, wherein the baseline performance measurement and thefirst post-clean performance measurement comprise a measurement of anintensity of light reflected by an object and/or a measurement ofattenuation of a signal strength.
 11. A method comprising: determining,by one or more processors, a baseline performance measurement associatedwith a clean performance baseline of a sensor; actuating a cleaningmechanism to remove at least a portion of an obstruction deposited onthe sensor; determining a first post-clean performance measurementassociated with the sensor; and determining a degradation measurementbetween the baseline performance measurement and the first post-cleanperformance measurement, wherein the degradation measurement indicatesan effectiveness of the cleaning mechanism, wherein the degradationmeasurement is a ratio between the baseline performance measurement andthe first post-clean performance measurement, and wherein the ratio isdetermined by dividing the first post-clean performance measurement bythe baseline performance measurement.
 12. The method of claim 11,wherein the clean performance baseline of the sensor is a state of thesensor without the obstruction.
 13. The method of claim 11, wherein theobstruction is at least one of mud, rain, bugs, or debris.
 14. Themethod of claim 11, wherein actuating the cleaning mechanism causes anapplication of fluid on the sensor, and causes an airflow configured toremove droplets of the fluid containing at least part of the obstructionfrom the sensor.
 15. The method of claim 11, wherein the obstruction israin droplets, and wherein actuating the cleaning mechanism causes anapplication of an airflow that removes rain droplets at an airflow ratethat exceeds a rain rate.
 16. The method of claim 11, wherein thedegradation measurement is a first degradation measurement, and furthercomprises: determining a second post-clean performance measurement aftera second obstruction is deposited on the sensor; determining a seconddegradation measurement based on the baseline performance measurementand the second post-clean performance measurement; and determining amean value or a standard deviation based on the first degradationmeasurement and the second degradation measurement.
 17. The method ofclaim 11, further comprising: determining a mean value or a standarddeviation of one or more ratio values determined between one or morebaseline performance measurements and one or more post-clean performancemeasurements is below a validation threshold; and determining that thecleaning mechanism is in a failed state.
 18. The method of claim 11,further comprising: determining a mean value or a standard deviation ofone or more ratio values determined between one or more baselineperformance measurements and one or more post-clean performancemeasurements is above a validation threshold; and determining that thecleaning mechanism is in a pass state.
 19. A non-transitorycomputer-readable medium storing computer-executable instructions whichwhen executed by one or more processors result in performing operationscomprising: determining a baseline performance measurement associatedwith a clean performance baseline of a sensor; actuating a cleaningmechanism to remove at least a portion of an obstruction deposited onthe sensor; determining a first post-clean performance measurementassociated with the sensor; and determining a degradation measurementbetween the baseline performance measurement and the first post-cleanperformance measurement, wherein the degradation measurement indicatesan effectiveness of the cleaning mechanism, wherein the degradationmeasurement is a ratio between the baseline performance measurement andthe first post-clean performance measurement, and wherein the ratio isdetermined by dividing the first post-clean performance measurement bythe baseline performance measurement.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the clean performancebaseline of the sensor is a state of the sensor without the obstruction.